Cross-sectional study to assess the impact of the COVID-19 pandemic on healthcare services and clinical admissions using statistical analysis and discovering hotspots in three regions of the Greater Toronto Area

Objectives The COVID-19 pandemic disrupted healthcare services, leading to the cancellation of non-urgent tests, screenings and procedures, a shift towards remote consultations, stalled childhood immunisations and clinic closures which had detrimental effects across the healthcare system. This study investigates the impact of the COVID-19 pandemic on clinical admissions and healthcare quality in the Peel, York and Toronto regions within the Greater Toronto Area (GTA). Design In a cross-sectional study, the negative impact of the pandemic on various healthcare sectors, including preventive and primary care (PPC), the emergency department (ED), alternative level of care (ALC) and imaging, procedures and surgeries is investigated. Study questions include assessing impairments caused by the COVID-19 pandemic and discovering hotspots and critical subregions that require special attention to recover. The measuring technique involves comparing the number of cases during the COVID-19 pandemic with before that, and determining the difference in percentage. Statistical analyses (Mann-Whitney U test, analysis of variance, Dunn’s test) is used to evaluate sector-specific changes and inter-relationships. Setting This work uses primary data which were collected by the Black Creek Community Health Centre. The study population was from three regions of GTA, namely, the city of Toronto, York and Peel. For all health sectors, the sample size was large enough to have a statistical power of 0.95 to capture 1% variation in the number of cases during the COVID-19 pandemic compared with before that. Results All sectors experienced a significant decline in patient volume during the pandemic. ALC admissions surged in some areas, while IPS patients faced delays. Surgery waitlists increased by an average of 9.75%, and completed IPS procedures decreased in several subregions. Conclusions The COVID-19 pandemic had a universally negative impact on healthcare sectors across various subregions. Identification of the hardest-hit subregions in each sector can assist health officials in crafting recovery policies.

Introduction #2.The introduction is slightly redundant and the author merely lists up existing research regarding the impact of COVID-19.Thus, please rewrite the section concisely.Methods #3.What does ALC (Alternative Level of Care) mean?#4.Please describe the study design.

Results
#5. Please demonstrate the proportion of missing data among all data #6.The study is a before-after design.Thus, the authors can not investigate causal relationships.This needs to be included in the limitation section.
Minor concerns #7.In the Methods section, the authors stated the STROB.It may indicate STROBE.

VERSION 1 -AUTHOR RESPONSE
Reviewer: 1 Dr. Raktim Swarnakar, All India Institute of Medical Sciences Comments to the Author: Dear authors, 1. Please discuss in terms of Generalisability of the study in the discussion section.

Response:
Thank you very much for this important comment.We have compared our results with the results from other countries in the discussion to discuss generalisability of our work: Our findings could be generalized to health services in other locations, since other studies have also found similar pattern of incidences in other countries.Pescariu, et al [46] found that intra-cardiac device implantation procedures decreased by 75% during the COVID-19 pandemic, in Romania.Quaquarini, et al [13] reported a significant decline in radiological exams during the COVID-19 pandemic, in Lombardy, Italy.Sutherland, et al [14] found that during the COVID-19 pandemic, breast screening decreased by 51.5%, emergency department visits by 13.9%, and hospital planned surgeries by 32.6% compared to before, in Australia.
2. What is the implications and new things of this study in the background of all available similar studies.

Response:
Thank you very much for this great comment.We have discussed the novelty and implications of our study in the discussion: Moreover, other studies found similar results in Ontario [16,19,20,23].However, our work is novel since it has studied the impact of COVID-19 on healthcare system in a more granular scale, i.e., subregions, to extract critical localities that require especial attention for recovery.
The COVID-19 pandemic has had a negative impact on all the health sectors under study.Patients with high acuity conditions such as chronic and non-communicable disease, mental health disorder, and patients that need urgent IPS services, that have postponed or missed their medication need to be ministered immediately, before they are too ill to be treated adequately.For some conditions, such as cancer, long waiting times increase the risk of developing advanced cancer and metastases.Therefore, based on the criticality of the condition, patients must be prioritized and recovery programs need to be informed.Setting: This work uses primary data which was collected by the Black Creek Community Health Center (BBCCHC).The study population was from three regions of GTA, namely, the city of Toronto, York, and Peel.For all health sectors, the sample size was large enough to have a statistical power of 0.95 to capture 1% variation in the number of cases during the COVID-19 pandemic compared to before that.
We have also altered the title to include the important parts of the study design: A cross-sectional study to assess the impact of the COVID-19 pandemic on healthcare services and clinical admissions using statistical analysis and discovering hotspots in three regions of the Greater Toronto Area The title includes the type of study (cross-sectional study), the questions of the study (assess the impact of COVID-19 pandemic on healthcare services and clinical admissions and discovering hotspots), the methods (which is using statistical analysis), and the population (which is in three regions of Greater Toronto Area).
Introduction #2.The introduction is slightly redundant and the author merely lists up existing research regarding the impact of COVID-19.Thus, please rewrite the section concisely.

Response:
Thank you very much for your helpful comment which remarkably increased the quality of our manuscript.We have made significantly changes to the introduction of the manuscript to explain the problem better, explain some ambiguous parts (e.g.ALC) and remove the redundancy: The novel coronavirus disease of 2019, known as COVID-19, originated in Wuhan, China, in December 2019, and quickly spread worldwide through international and intra/interstate travel.Shortly thereafter, hospitals and Intensive Care Units (ICUs) were inundated with COVID-19 patients.Many countries implemented lockdown measures to curb the rapid spread of the virus and protect lives.Although Non-Pharmaceutical Interventions (NPI) successfully reduced the number of COVID-19 infections, they had a destructive outcome for the healthcare system [1,2].Numerous surgeries, laboratory tests and non-emergency healthcare services were cancelled or postponed.Additionally, people delayed visiting healthcare centers, fearing coronavirus infection, and the associated stigma of testing positive for the virus [3].Consequently, many patients awaiting for healthcare services have fallen out of their timeline, and need to be sorted.
The COVID-19 pandemic had a devastating impact on patients with chronic conditions and Non-Communicable Diseases (NCD), since they were barely able to meet the medical care that they required [4,5].Equally, mental health disorder increased in adults as well as children, and adolescents, whom due to the situation did not get medical treatment, and therefore, need long-term attention to recover [6,7].Therefore, it is paramount to identify locations where primary health care and Emergency Department (ED) visits have significantly decreased in them during the COVID-19 pandemic to provide appropriate medication to individuals that require persistent care and attention.
Another concerning issue that occurred during the COVID-19 pandemic is decline in childhood immunization.Disruption in children's vaccination, even for a short period of time, could rise the susceptibility to Vaccine Preventable Diseases (VPD) such as measles, polio, and pertussis, and increase the risk of VPD outbreaks [8,9].It is necessary to study this critical matter and provide families with sustained catch-up programs to rectify missing doses.
Despite urgent need to hospital beds, Alternative Level of Care (ALC) dramatically increased in some places, during the COVID-19 pandemic [10,11].ALC is the term used to address patients that occupy a hospital bed, but no longer need the intensity of care provided.The most common reasons for the discharge delay in Canada include palliative needs, physical therapy, and requiring transition to another adequate facility [12].ALC is a costly issue which is considered as a bottleneck to hospital We have clearly separated and explained the six aspects of study design [43]: the question to be answered, the study population, the type of study, the unit of observation, the measuring technique, the calculation of sample size: In the following, all aspects of the study design are thoroughly described [40].

Questions to be answered
This work is fundamentally beneficial to the policy makers and health officials working at the GTA to understand the damages caused by the COVID-19 pandemic to the health system, plan for recovery, and prepare for future pandemics.Five questions are answered in this work:   Roughly, half of the population are Female (53%).Patients are from all age groups.Table 1 shows the total population in each subregion, and the percentage of people that are 65 years old or above.

Type of study and unit of observation
This study consumes primary data and is considered as an observational epidemiological study, and a cross-sectional study.The unit of analysis for primary healthcare, cancer screenings (fecal, mammogram, and pap smear), and ED (substance use and mental health) is the number of visits, and for childhood immunization, ALC, and IPS sector (patients who have exceeded their time targe, patients who have completed their IPS, and patients waiting for surgery) is the number of patients.

Measuring technique
The For certain parameters within the dataset, we compared the number of patients during the COVID-19 pandemic to the period before that for each sub-region, using Equation 1. ( Where, and are the number of patients during and before COVID-19 periods, respectively.So, if the number of patients has increased during COVID-19 compared to before that, equation 1 will return a positive and if it has decreased, it will return a negative value.
Missing data which happened only in the PPC sector due to unavailability of the location variable in the OHIP data source, accounted for approximately 12% of the PPC data, and was excluded from the dataset.
The health parameters of each health sector were compared across different sub-regions using histograms, box and violin plots, and through the Mann-Whitney U test.To identify which sub-region is significantly different from the rest, first, ANOVA was used to determine the significance, and then Dunn's test with Bonferroni correction was applied to identify the exact sub-region(s).P-values lower than 0.05 are considered significant [42].This study uses the STROBE cross sectional reporting guidelines [43].Patients or the public were not involved in the design, or reporting, or dissemination plans of this research.

Calculation of sample size
The sample size of the study was set large enough to have a statistical power of 0.95 for capturing 1% variation in the number of cases, during COVID-19 pandemic compared to before that, for all health parameters under study [44].Table 1 illustrates the total population size for different subregions.

Figure 1
illustrates the study area, with the pink areas representing the FSAs under investigation.The dashed lines, bold black lines, and bold red lines demarcate the boundaries of the FSAs, sub-regions, and regions, respectively.We have created this map using ArcGis Online [41].

Figure 1 :
Figure 1: The area under study data were collected from various sources, including the Ontario Health Insurance Plan (OHIP), Client and Health Related Information System (CHRIS), Transfer Payment Ontario (TPON), Ontario Drug Benefit Claims (ODBC), Drug and Alcohol Treatment Information System (DATOS), Narcotics Monitoring System, Bed Census Summary (BCS), Ontario Healthcare Financial and Statistics (OHFS), Wait Time Information System (WTIS), Resource Matching and Referral (RM&R), Home and Community Care Support Services, among others.

2 .
What does "3895 Toronto" at line 31, page 4, mean?Is it the number of patients?Response: Thank you very much for you attention.3895 is the master number of Toronto public health Unit.We have clarified this in our manuscript to fix this concern.
• Toronto Public Health Unit (Master No. 3895) • York Region Public Health Unit (Master No. 2270) • Peel Public Health Unit (Master No. 2253) 3. The manuscript would benefit if the authors would use a subtitle to highlight the statistical analyse.Response:Thank you very much for this great comment.We have added a subtitle to briefly explain all the statistical methods and techniques used for evaluating the damages caused by the COVID-19 pandemic.Subtitle:

Assessing the ripple effect: using Mann-Whitney U test, histograms, box and violon plots to compare the admissions of different health sectors in the Greater Toronto Area, during the COVID-19 pandemic with before that, and ANOVA and Dunn's test with Bonferroni correction to extract the critical sub-regions
Approximately half of the locations, the number of" should be rephrased, page 9, line 15.Overall, the Discussion section is too short.The study would benefit if the authors would compare their findings with research from other countries, for example: Pescariu Silvius Alexandru, Cristina Tudoran, Gheorghe Nicusor Pop, Sorin Pescariu, Romulus Zorin Timar, and Mariana Tudoran.2021."Impact of COVID-19 Pandemic on the Implantation of Intra-Cardiac Devices in Diabetic and Non-decreased by 75% during the COVID-19 pandemic, in Romania.Quaquarini, et al [13] reported a significant decline in radiological exams during the COVID-19 pandemic, in Lombardy, Italy.Sutherland, et al[14]found that during the COVID-19 pandemic, breast screening decreased by 51.5%, emergency department visits by 13.9%, and hospital planned surgeries by 32.6% compared to before, in Australia.Moreover, other studies found similar results in Ontario[16, 19, 20, 23].However, our work is novel since it has studied the impact of COVID-19 on healthcare system in a more granular scale, i.e., subregions, to extract critical localities that require especial attention for recovery.[46]Pescariu SA, Tudoran C, Pop GN, Pescariu S, Timar RZ, Tudoran M, Impact of COVID-19 Pandemic on the Implantation of Intra-Cardiac Devices in Diabetic and Non-Diabetic Patients in the Western of Romania, Medicina (Kaunas), 2021;57(5) doi: 10.3390/medicina57050441. 9. Usually Limitations is at the end of Discussions Response: Thank you very much for your attention and notification.We have removed the "Strengths and Limitations" and added it to the end of the discussion.
Response:Thank you very much for your attention.We had forgotten a preposition in that sentence.It is fixed now: Approximately, in half of the locations, the number of completed procedures for cancer tests, MRI scans, and CT scans increased during the COVID-19 pandemic 7.In Discussion the authors mention that the number of CT scans decreased.I suggest to debate more on this affirmation, as the number of lung CT scans was influenced by the need to classify the lung injury in patients diagnosed with COVID-19 and angio-CT was frequently used to confirm or inform a suspicion of pulmonary embolism, with a high incidence in hospitalized, sever COVID-19 cases, as stated in Tudoran Cristina, Dana Emilia Velimirovici, Delia Mira Berceanu-Vaduva, Maria Rada, Florica Voiţă-Mekeres, and Mariana Tudoran.2022."IncreasedSusceptibilityforThromboembolicEventsversusHighBleedingRiskAssociatedwithCOVID-19" Microorganisms 10, no.9: 1738.https://doi.org/10.3390/microorganisms10091738Response:Thankyouverymuchforyourattention.This is an interesting debate and we have added it to the end of the results section: Interestingly, the results indicate that although the number of completed CT scans has increased by 2.38% on average during the COVID-19 pandemic compared to before that (Table5), the number of patients who have surpassed their timeline increased the most for CT scan among all other ISPs, according to Table4.The reason lies in the high demand for CT scan during the pandemic to classify lung injury in patients diagnosed with COVID-19.Moreover, angio-CT was frequently used during the pandemic to confirm or inform suspicious pulmonary embolism in severe COVID-19 cases[45].[45]TudoranC,VelimiroviciDE,Berceanu-Vaduva DM, Rada M, Voiţă-Mekeres F, Tudoran M, Increased Susceptibility for Thromboembolic Events versus High Bleeding Risk Associated with COVID-19, Microorganisms, 2022;10(9) doi: 10.3390%2Fmicroorganisms10091738.8. the discussion and compared our work with other works:Our findings could be generalized to health services in other locations, since other studies have also found similar pattern of incidences in other countries.Pescariu, et al[46]found that intra-cardiac device implantation procedures

Makoto Kaneko, Yokohama City University, Hamamatsu University School of Medicine Comments to the Author
Thank you very much for this great comment.We have included the study design with common terms in the abstract: Design: In a cross-sectional study, the negative impact of the pandemic on various healthcare sectors, including Preventive and Primary Care (PPC), the Emergency Department (ED), Alternative Level of Care (ALC), and Imaging, Procedures, and Surgeries (IPS) is investigated.Study questions include assessing impairments caused by the COVID-19 pandemic and discovering hotspots and critical sub-regions that require especial attention to recover.The measuring technique involves comparing the number of cases during the COVID-19 pandemic with before that, and determining the difference in percentage.Statistical analyses (Mann-Whitney U, ANOVA, Dunn tests) is used to evaluate sector-specific changes and interrelationships.
:To the authors Thank you for providing the opportunity to review the article.My below comments are based on the STROBE checklist for observational study (http://www.equatornetwork.org/reporting-guidelines/strobe/)andIhope these comments improve your article.Major concernsTitle and abstract #1.Please include the study's design with a commonly used term in the title or the abstract.Response: [10] Mathews K, Podlog M, Greenstein J, Cioè-Peña E, Cambria B, Ardolic B, et al, Development and Implementation of an Alternate Care Site During the COVID-19 Pandemic, Cureus, 2020;12(10): e10799, doi: 10.7759%2Fcureus.10799.[11] Delgado RC, Quesada PP, García EP, Zabalza IM, Vázquez M, Balín RE, Alternate Care Sites for COVID-19 Patients: Experience from the H144 Hospital of the Health Service of the Principality of Thank you very much for this important comment.We have made substantial changes to the methodology of the manuscript to clearly describe the study design [43].[43] Röhrig B, Prel JB, Blettner M, Study Design in Medical Research, Dtsch Arztebl Int, 2009;106(11):184-189, doi: 10.3238%2Farztebl.2009.0184.
By what percentage has ALC increased, on average during the COVID-19 pandemic compared to before that, and where are the hotspots in terms of sub-regions?3.For the IPSs listed below, by what percentage the number of patients who exceeded their time target increased on average, during the COVID-19 pandemic compared to before that, and where are the hotspots of this incident in terms of sub-regions?4.For the IPSs listed below, by what percentage the completed cases decreased on average, By what percentage the number of patients waiting for surgery has increased on average, during the COVID-19 pandemic compared to before that, and where are the hotspots in terms of sub-regions?2.2.Study PopulationThe data is concentrated on the York and Peel regions, as well as the city of Toronto, encompassing various sub-regions, including Bramalea, Brampton, Dufferin, East Mississauga, East Toronto, Eastern York Region, Mid-East Toronto, Mid-West Toronto, North Etobicoke Malton West Woodbridge, North Toronto, North York Central, North York West, North West Mississauga, Northern York Region, Scarborough North, Scarborough South, South Etobicoke, South West Mississauga, West Toronto, and Western York Region.The data was collected for 151 different Forward Sortation Areas (FSAs).

Table 1 :
The population size and percentage of patients with 65 years of age or over